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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "## Objective\n",
    "\n",
    "I wanted to explore the effect that different amounts of increasing genes and differing strengths of the increasing genes have on the scores in growth screens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "using DataFrames\n",
    "using Gadfly\n",
    "using ColorBrewer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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       "<table class=\"data-frame\"><tr><th></th><th>measure</th><th>score</th><th>num_genes</th><th>coverage</th><th>representation</th><th>moi</th><th>seq_depth</th><th>bottleneck_representation</th><th>num_bottlenecks</th><th>noise</th><th>frac_hq</th><th>mean_hq_kd</th><th>frac_inc</th><th>frac_dec</th><th>cent_inc_phenotype</th><th>relationships</th><th>crisprtype</th><th>run_idx</th></tr><tr><th>1</th><td>inc</td><td>0.06020381257093152</td><td>500</td><td>5</td><td>100</td><td>0.25</td><td>100</td><td>100</td><td>1</td><td>0.01</td><td>0.9</td><td>0.9</td><td>0.02</td><td>0.1</td><td>0.1</td><td>linearsigmoidal</td><td>CRISPRi</td><td>1</td></tr><tr><th>2</th><td>inc</td><td>0.2276457759409211</td><td>500</td><td>5</td><td>100</td><td>0.25</td><td>100</td><td>100</td><td>1</td><td>0.01</td><td>0.9</td><td>0.9</td><td>0.02</td><td>0.1</td><td>0.1</td><td>linearsigmoidal</td><td>CRISPRi</td><td>2</td></tr><tr><th>3</th><td>inc</td><td>0.10604274469914624</td><td>500</td><td>5</td><td>100</td><td>0.25</td><td>100</td><td>100</td><td>1</td><td>0.01</td><td>0.9</td><td>0.9</td><td>0.02</td><td>0.1</td><td>0.1</td><td>linearsigmoidal</td><td>CRISPRi</td><td>3</td></tr><tr><th>4</th><td>inc</td><td>0.22750659291135192</td><td>500</td><td>5</td><td>100</td><td>0.25</td><td>100</td><td>100</td><td>1</td><td>0.01</td><td>0.9</td><td>0.9</td><td>0.02</td><td>0.1</td><td>0.1</td><td>linearsigmoidal</td><td>CRISPRi</td><td>4</td></tr><tr><th>5</th><td>inc</td><td>0.08822893395390556</td><td>500</td><td>5</td><td>100</td><td>0.25</td><td>100</td><td>100</td><td>1</td><td>0.01</td><td>0.9</td><td>0.9</td><td>0.02</td><td>0.1</td><td>0.1</td><td>linearsigmoidal</td><td>CRISPRi</td><td>5</td></tr><tr><th>6</th><td>inc</td><td>0.08335054245099988</td><td>500</td><td>5</td><td>100</td><td>0.25</td><td>100</td><td>100</td><td>1</td><td>0.01</td><td>0.9</td><td>0.9</td><td>0.02</td><td>0.1</td><td>0.1</td><td>linearsigmoidal</td><td>CRISPRi</td><td>6</td></tr></table>"
      ],
      "text/plain": [
       "6×18 DataFrames.DataFrame\n",
       "│ Row │ measure │ score     │ num_genes │ coverage │ representation │ moi  │\n",
       "├─────┼─────────┼───────────┼───────────┼──────────┼────────────────┼──────┤\n",
       "│ 1   │ \"inc\"   │ 0.0602038 │ 500       │ 5        │ 100            │ 0.25 │\n",
       "│ 2   │ \"inc\"   │ 0.227646  │ 500       │ 5        │ 100            │ 0.25 │\n",
       "│ 3   │ \"inc\"   │ 0.106043  │ 500       │ 5        │ 100            │ 0.25 │\n",
       "│ 4   │ \"inc\"   │ 0.227507  │ 500       │ 5        │ 100            │ 0.25 │\n",
       "│ 5   │ \"inc\"   │ 0.0882289 │ 500       │ 5        │ 100            │ 0.25 │\n",
       "│ 6   │ \"inc\"   │ 0.0833505 │ 500       │ 5        │ 100            │ 0.25 │\n",
       "\n",
       "│ Row │ seq_depth │ bottleneck_representation │ num_bottlenecks │ noise │\n",
       "├─────┼───────────┼───────────────────────────┼─────────────────┼───────┤\n",
       "│ 1   │ 100       │ 100                       │ 1               │ 0.01  │\n",
       "│ 2   │ 100       │ 100                       │ 1               │ 0.01  │\n",
       "│ 3   │ 100       │ 100                       │ 1               │ 0.01  │\n",
       "│ 4   │ 100       │ 100                       │ 1               │ 0.01  │\n",
       "│ 5   │ 100       │ 100                       │ 1               │ 0.01  │\n",
       "│ 6   │ 100       │ 100                       │ 1               │ 0.01  │\n",
       "\n",
       "│ Row │ frac_hq │ mean_hq_kd │ frac_inc │ frac_dec │ cent_inc_phenotype │\n",
       "├─────┼─────────┼────────────┼──────────┼──────────┼────────────────────┤\n",
       "│ 1   │ 0.9     │ 0.9        │ 0.02     │ 0.1      │ 0.1                │\n",
       "│ 2   │ 0.9     │ 0.9        │ 0.02     │ 0.1      │ 0.1                │\n",
       "│ 3   │ 0.9     │ 0.9        │ 0.02     │ 0.1      │ 0.1                │\n",
       "│ 4   │ 0.9     │ 0.9        │ 0.02     │ 0.1      │ 0.1                │\n",
       "│ 5   │ 0.9     │ 0.9        │ 0.02     │ 0.1      │ 0.1                │\n",
       "│ 6   │ 0.9     │ 0.9        │ 0.02     │ 0.1      │ 0.1                │\n",
       "\n",
       "│ Row │ relationships     │ crisprtype │ run_idx │\n",
       "├─────┼───────────────────┼────────────┼─────────┤\n",
       "│ 1   │ \"linearsigmoidal\" │ \"CRISPRi\"  │ 1       │\n",
       "│ 2   │ \"linearsigmoidal\" │ \"CRISPRi\"  │ 2       │\n",
       "│ 3   │ \"linearsigmoidal\" │ \"CRISPRi\"  │ 3       │\n",
       "│ 4   │ \"linearsigmoidal\" │ \"CRISPRi\"  │ 4       │\n",
       "│ 5   │ \"linearsigmoidal\" │ \"CRISPRi\"  │ 5       │\n",
       "│ 6   │ \"linearsigmoidal\" │ \"CRISPRi\"  │ 6       │"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df = readtable(\"../data/test_17c6a3d_dirty.csv\")\n",
    "df = readtable(\"bigiron_growth_sensitivity_to_lib_49b2f59_clean.csv\")\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[:weight_increasing] = df[:cent_inc_phenotype] .* 10 .* df[:frac_inc];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<table class=\"data-frame\"><tr><th></th><th>measure</th><th>cent_inc_phenotype</th><th>frac_inc</th><th>crisprtype</th><th>num_bottlenecks</th><th>std_score</th><th>mean_score</th><th>score_max</th><th>score_min</th><th>n</th></tr><tr><th>1</th><td>dec</td><td>0.1</td><td>0.02</td><td>CRISPRKO</td><td>1</td><td>0.08103675281224705</td><td>0.48407175518106366</td><td>0.5158381622834645</td><td>0.4523053480786628</td><td>25</td></tr><tr><th>2</th><td>dec</td><td>0.1</td><td>0.02</td><td>CRISPRKO</td><td>2</td><td>0.05708965871400093</td><td>0.7588929232930307</td><td>0.781272069508919</td><td>0.7365137770771424</td><td>25</td></tr><tr><th>3</th><td>dec</td><td>0.1</td><td>0.02</td><td>CRISPRKO</td><td>3</td><td>0.03810614475307261</td><td>0.864182893297521</td><td>0.8791205020407254</td><td>0.8492452845543165</td><td>25</td></tr><tr><th>4</th><td>dec</td><td>0.1</td><td>0.02</td><td>CRISPRKO</td><td>4</td><td>0.032278583732236646</td><td>0.9223997835127246</td><td>0.9350529883357613</td><td>0.9097465786896878</td><td>25</td></tr><tr><th>5</th><td>dec</td><td>0.1</td><td>0.02</td><td>CRISPRKO</td><td>5</td><td>0.02215787440224545</td><td>0.9429133700265189</td><td>0.951599256792199</td><td>0.9342274832608387</td><td>25</td></tr><tr><th>6</th><td>dec</td><td>0.1</td><td>0.02</td><td>CRISPRKO</td><td>6</td><td>0.015818632506021266</td><td>0.9556235629463546</td><td>0.961824466888715</td><td>0.9494226590039943</td><td>25</td></tr></table>"
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       "6×10 DataFrames.DataFrame\n",
       "│ Row │ measure │ cent_inc_phenotype │ frac_inc │ crisprtype │ num_bottlenecks │\n",
       "├─────┼─────────┼────────────────────┼──────────┼────────────┼─────────────────┤\n",
       "│ 1   │ \"dec\"   │ 0.1                │ 0.02     │ \"CRISPRKO\" │ 1               │\n",
       "│ 2   │ \"dec\"   │ 0.1                │ 0.02     │ \"CRISPRKO\" │ 2               │\n",
       "│ 3   │ \"dec\"   │ 0.1                │ 0.02     │ \"CRISPRKO\" │ 3               │\n",
       "│ 4   │ \"dec\"   │ 0.1                │ 0.02     │ \"CRISPRKO\" │ 4               │\n",
       "│ 5   │ \"dec\"   │ 0.1                │ 0.02     │ \"CRISPRKO\" │ 5               │\n",
       "│ 6   │ \"dec\"   │ 0.1                │ 0.02     │ \"CRISPRKO\" │ 6               │\n",
       "\n",
       "│ Row │ std_score │ mean_score │ score_max │ score_min │ n  │\n",
       "├─────┼───────────┼────────────┼───────────┼───────────┼────┤\n",
       "│ 1   │ 0.0810368 │ 0.484072   │ 0.515838  │ 0.452305  │ 25 │\n",
       "│ 2   │ 0.0570897 │ 0.758893   │ 0.781272  │ 0.736514  │ 25 │\n",
       "│ 3   │ 0.0381061 │ 0.864183   │ 0.879121  │ 0.849245  │ 25 │\n",
       "│ 4   │ 0.0322786 │ 0.9224     │ 0.935053  │ 0.909747  │ 25 │\n",
       "│ 5   │ 0.0221579 │ 0.942913   │ 0.951599  │ 0.934227  │ 25 │\n",
       "│ 6   │ 0.0158186 │ 0.955624   │ 0.961824  │ 0.949423  │ 25 │"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_info = by(df, [:measure, :cent_inc_phenotype, :frac_inc, :crisprtype, :num_bottlenecks]) do grouped_df\n",
    "    n = size(grouped_df, 1)\n",
    "    mean_score = clamp(mean(grouped_df[:score]), 0, 1)\n",
    "    std_score = std(grouped_df[:score])\n",
    "    conf_int = 1.96 * std_score./sqrt(n)\n",
    "    DataFrame(\n",
    "        std_score = std_score,\n",
    "        mean_score = mean_score,\n",
    "        score_max = mean_score + conf_int,\n",
    "        score_min = mean_score - conf_int,\n",
    "        n = n\n",
    "    )\n",
    "end\n",
    "head(grouped_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "p = plot(grouped_info[grouped_info[:frac_inc] .== 0.02, :], \n",
    "x=:num_bottlenecks, y=:mean_score, ymax=:score_max, ymin=:score_min, \n",
    "xgroup=:crisprtype, ygroup=:measure, color=:cent_inc_phenotype,\n",
    "Geom.subplot_grid(Geom.line, Geom.ribbon, Coord.cartesian(xmin=1, ymax=1, ymin=0),\n",
    "Guide.xticks(ticks=[1,5,10,15,20])),\n",
    "Guide.title(\"Growth screens with more bottlenecks are more sensitive\\n to takeover by strong increasing genes\"))\n",
    "filename = \"../plots/growth_screen_takeover_increasing_genes\"\n",
    "draw(SVG(\"$filename.svg\", 15cm, 10cm), p)\n",
    "p.theme = Theme(background_color = colorant\"white\")\n",
    "draw(PNG(\"$filename.png\", 15cm, 10cm, dpi=300), p)\n",
    "draw(SVG(15cm, 10cm), p)"
   ]
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   "execution_count": null,
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   "source": []
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